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Ego-Motion Classification for Body-Worn Videos

  • Zhaoyi Meng
  • Javier Sánchez
  • Jean-Michel Morel
  • Andrea L. BertozziEmail author
  • P. Jeffrey Brantingham
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Portable cameras record dynamic first-person video footage and these videos contain information on the motion of the individual to whom the camera is mounted, defined as ego. We address the task of discovering ego-motion from the video itself, without other external calibration information. We investigate the use of similarity transformations between successive video frames to extract signals reflecting ego-motions and their frequencies. We use novel graph-based unsupervised and semi-supervised learning algorithms to segment the video frames into different ego-motion categories. Our results show very accurate results on both choreographed test videos and ego-motion videos provided by the Los Angeles Police Department.

Notes

Acknowledgements

The work was supported by the ONR grant N00014-16-1-2119, NSF grant DMS-1737770, NSF grant DMS-1417674, FUI project Plein Phare by BPI-France and NIJ Grant 2014-R2-CX-0101.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Zhaoyi Meng
    • 1
  • Javier Sánchez
    • 2
  • Jean-Michel Morel
    • 3
  • Andrea L. Bertozzi
    • 1
    Email author
  • P. Jeffrey Brantingham
    • 1
  1. 1.University of California, Los AngelesLos AngelesUSA
  2. 2.Universidad de Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain
  3. 3.Ecole Normale Supérieure de CachanCachanFrance

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